[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4243":3,"related-4243":92},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":64,"slug":67,"view_count":21,"doi":68,"paper":69,"created_at":91},4243,"Digital corporate responsibility as an element of corporate social responsibility (a case study of Ukrainian agricultural enterprises)","https:\u002F\u002Fdoi.org\u002F10.36887\u002F2524-0455-2026-4-49","The article aims to substantiate the theoretical, methodological, and practical foundations of Digital Corporate Responsibility (DCR\u002FCDR) as a direction of Corporate Social Responsibility (CSR) amid the comprehensive digitalization of business processes, focusing on Ukraine’s agricultural sector. The study applies dialectical analysis and synthesis, grouping, structural-functional analysis, and case study methods. The results show that digitalization creates significant opportunities to improve the efficiency, productivity, and profitability of agribusiness while generating new ethical, social, and environmental risks, including cybersecurity threats, algorithmic bias, digital exclusion, the carbon footprint of IT infrastructure, and electronic waste. DCR is therefore considered an important mechanism for mitigating these risks and ensuring socially and environmentally responsible digital transformation. The scientific novelty lies in substantiating DCR within three interrelated ESG dimensions: digital environmental (E), digital social (S), and digital governance (G) responsibility. The study shows that implementing DCR can strengthen stakeholder relations, transparency, trust, and the sustainable development potential of agricultural companies. The experience of leading Ukrainian agricultural holdings, including Astarta and Kernel, illustrates practical approaches to combining digital technologies with responsible business practices. The research’s practical significance lies in the proposed use of corporate digital ethics codes to strengthen cybersecurity, protect digital rights, develop digital culture, and promote responsible digital behavior among employees and stakeholders. Further research should focus on developing a comprehensive system of indicators to assess the level of DCR, identifying relevant ESG criteria, and adapting international approaches and best practices to the institutional and socio-economic conditions of Ukrainian agribusiness. Keywords: Digital Corporate Responsibility, Corporate Social Responsibility, digitalization, digital transformation, ESG, agricultural sector, agribusiness, precision agriculture, digital ethics, sustainable development, cybersecurity, stakeholders.","本文旨在论证在企业流程全面数字化背景下，数字企业责任（Digital Corporate Responsibility, DCR\u002FCDR）作为企业社会责任（Corporate Social Responsibility, CSR）一个发展方向的理论、方法论与实践基础，并以乌克兰农业部门为聚焦对象。研究采用辩证分析与综合、分组法、结构功能分析以及案例研究等方法。结果表明，数字化为提升农业企业的效率、生产率和盈利能力创造了重要机遇，同时也带来了新的伦理、社会和环境风险，包括网络安全威胁、算法偏见、数字排斥、IT基础设施的碳足迹以及电子废弃物。因此，数字企业责任被视为缓解这些风险、确保社会与环境负责任的数字化转型的重要机制。本研究的科学新颖性在于从三个相互关联的ESG维度论证数字企业责任：数字环境（E）责任、数字社会（S）责任和数字治理（G）责任。研究表明，实施数字企业责任能够加强利益相关者关系、透明度、信任以及农业公司的可持续发展潜力。乌克兰领先农业控股公司，包括Astarta和Kernel的经验，展示了将数字技术与负责任商业实践相结合的实际路径。研究的实践意义在于提出利用企业数字伦理准则来加强网络安全、保护数字权利、发展数字文化，并促进员工和利益相关者负责任的数字行为。未来研究应着重开发评估数字企业责任水平的综合指标体系，确定相关ESG标准，并使国际方法和最佳实践适应乌克兰农业企业的制度与社会经济条件。关键词：数字企业责任，企业社会责任，数字化，数字化转型，ESG，农业部门，农业企业，精准农业，数字伦理，可持续发展，网络安全，利益相关者。",null,"Actual problems of innovative economy and law","2026-10-04T00:00:00Z","论文",10,false,55,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":17,"relevant":21,"comment":22},8,18,15,6,1,"乌克兰农业企业数字企业责任的理论与案例研究，提出数字环境、数字社会、数字治理三维ESG框架，对农业数字化风险治理有参考价值，但属境外期刊论文、影响范围有限。",[24],{"name":10,"url":6},[26,27,28,29,30],"数字农业","网络安全","乌克兰农业","农业ESG","数字伦理",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"H4","carbon","农业碳与绿色低碳","\u002Ftopic\u002Fenvironment\u002Fcarbon","H",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":47},[44,53,59],{"code":45,"score":46,"supported":47,"title":48,"abstract":49,"tags":52},"I1",7,true,[],[50,51],"精准农业","precision agriculture",[26],{"code":32,"score":20,"supported":47,"title":54,"abstract":55,"tags":58},[],[56,57],"碳足迹","carbon footprint",[],{"code":60,"score":21,"supported":14,"title":61,"abstract":62,"tags":63},"I6",[],[],[27],[65,66],"Astarta Kernel 数字责任","乌克兰农业企业 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Vasylyna","https:\u002F\u002Forcid.org\u002F0000-0003-2124-3792",0,"https:\u002F\u002Fapie.org.ua\u002Fwp-content\u002Fuploads\u002F2026\u002F10\u002Fapie_2026_r04_a49.pdf",{"tldr":84,"method":85,"finding":86,"direction":87,"opportunity":88},"研究乌克兰农业企业数字化中的数字企业责任，将其作为CSR新方向并构建ESG三维框架。","辩证分析、案例研究法，以Astarta和Kernel等乌克兰农业控股公司为例。","数字化带来效率提升的同时产生网络安全、算法偏见、数字排斥等新风险，需DCR机制应对。","农业绿色发展与碳","可开发DCR评估指标体系，将数字伦理与ESG标准适配到不同国家农业情境中。","数字乡村与农业信息化","openalex","2026-10-05T23:30:31.062780Z",{"total":20,"page":21,"page_size":20,"items":93},[94,149,198,252,324,440],{"id":95,"title":96,"url":97,"summary":98,"summary_zh":9,"content":9,"source_name":99,"source_url":9,"published_at":100,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":101,"score_detail":102,"sources":107,"tags":109,"primary_topic":113,"secondary_topics":114,"classification":115,"search_phrases":135,"slug":138,"view_count":139,"doi":9,"paper":140,"created_at":148},1962,"Digital Technology Empowering Agricultural Green Transformation and Low-Carbon Development in China","https:\u002F\u002Fem.swfu.edu.cn\u002Finfo\u002F1791\u002F20211.htm","宋闻问、唐勇辉、李宇硕、潘立（西南林业大学）基于中国31个省（自治区、直辖市）2010—2023年面板数据，运用固定效应模型、中介效应模型与门槛效应模型系统探究数字技术对农业碳排放强度的影响。结果显示：(1)数字技术对农业碳排放强度具有显著且稳健的抑制作用。(2)数字技术通过强化环境规制水平和提升农业社会化服务水平两条核心路径实现碳减排。(3)减排效应存在农业产业集聚门槛特征，集聚水平提升时边际效应递增。(4)在粮食主产区与产销平衡区抑制效应显著，粮食主销区不显著。(5)在低地形起伏地区抑制效应显著，高地形起伏地区则不显著。","Sustainability 2026(4) 西南林业大学经济管理学院","2026-04-01T00:00:00Z",73,{"impact":103,"substance":104,"depth":18,"authority":105,"freshness":21,"relevant":21,"comment":106},20,22,12,"基于省级面板数据的实证研究，揭示数字技术抑制农业碳排放的机制与异质性，对农业绿色转型有参考价值。",[108],{"name":99,"url":97},[26,110,111,112],"农业社会化服务","碳减排","环境规制",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":116},{"candidates":117,"agricultural_evidence":47},[118,124,131],{"code":32,"score":20,"supported":47,"title":119,"abstract":120,"tags":123},[],[121,122],"碳排放","农业碳",[],{"code":125,"score":126,"supported":47,"title":127,"abstract":128,"tags":130},"G2",4,[],[129],"社会化服务",[110],{"code":45,"score":21,"supported":14,"title":132,"abstract":133,"tags":134},[],[],[26],[136,137],"农业社会化服务 数字农业 环境规制 碳减排","农业社会化服务 数字农业","农业社会化服务数字农业环境规制碳减排-1962",2,{"doi":9,"openalex_id":9,"authors":141,"venue":9,"cited_by_count":81,"oa_url":9,"card":142,"direction":87,"ingested_from":147},[],{"tldr":143,"method":144,"finding":145,"direction":87,"opportunity":146},"研究数字技术对中国农业碳排放强度的影响及机制。","基于2010-2023年省级面板数据，运用固定效应、中介效应和门槛效应模型。","数字技术显著抑制农业碳排放，通过强化环境规制和提升农业社会化服务实现，且存在产业集聚门槛和区域异质性","可探究数字技术在不同农业产业集聚阶段和地形条件下的差异化减排机制，以及如何优化政策以促进粮食主销区和高地形起伏地区的减排","agent","2026-09-09T00:03:57.053476Z",{"id":150,"title":151,"url":152,"summary":153,"summary_zh":9,"content":9,"source_name":154,"source_url":9,"published_at":155,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":156,"score_detail":157,"sources":160,"tags":162,"primary_topic":166,"secondary_topics":167,"classification":168,"search_phrases":185,"slug":188,"view_count":189,"doi":9,"paper":190,"created_at":197},1585,"Digital Farming Slashes China's Carbon Footprint: Provincial-Level Panel Evidence from 2012 to 2023 (Frontiers in Sustainable Food Systems)","https:\u002F\u002Fagritechinsights.com\u002Findex.php\u002F2026\u002F09\u002F02\u002Fdigital-farming-slashes-chinas-carbon-footprint","由江西理工学院 Yi Zhang 领衔的研究团队在《Frontiers in Sustainable Food Systems》发表论文，运用 2012—2023 年省级面板数据，构建数字农业发展指数衡量数字基础设施、人力资本、产业支持等维度。研究发现：数字农业采纳程度较高的地区农业碳排放强度显著下降；通过提升资本配置效率（即改善投资流向，缓解对高排放机械的过度投资和对精准工具的利用不足）间接降低碳排放；空间分析显示数字农业存在跨区域溢出效应，主粮产区和东部地区效应更强，但中西部地区效益尚不显著。文章发表于 2026-09-02 综述解读。","Frontiers in Sustainable Food Systems","2026-09-02T00:00:00Z",78,{"impact":18,"substance":104,"depth":18,"authority":158,"freshness":46,"relevant":21,"comment":159},13,"基于省级面板数据的实证研究，揭示数字农业对碳减排的显著作用及区域差异，为政策制定提供科学依据。",[161],{"name":154,"url":152},[26,163,164,165],"空间溢出","省级面板","农业碳排放",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":169},{"candidates":170,"agricultural_evidence":47},[171,175,179],{"code":32,"score":19,"supported":47,"title":172,"abstract":173,"tags":174},[57],[121,122],[165],{"code":45,"score":126,"supported":47,"title":176,"abstract":177,"tags":178},[],[26],[26],{"code":180,"score":126,"supported":47,"title":181,"abstract":182,"tags":184},"I4",[],[183],"空间分析",[163],[186,187],"农业碳排放 数字农业 省级面板 空间溢出","农业碳排放 数字农业","农业碳排放数字农业省级面板空间溢出-1585",3,{"doi":9,"openalex_id":9,"authors":191,"venue":9,"cited_by_count":81,"oa_url":9,"card":192,"direction":87,"ingested_from":147},[],{"tldr":193,"method":194,"finding":195,"direction":87,"opportunity":196},"利用省级面板数据，发现数字农业显著降低农业碳排放强度，存在区域异质性。","构建数字农业发展指数，采用省级面板数据与空间分析方法。","数字农业通过提升资本配置效率降低碳排放，主粮产区和东部效应更强。","中西部数字农业减排效应不显著，可探索其制约因素及针对性政策设计。","2026-09-04T00:05:34.351468Z",{"id":199,"title":200,"url":201,"summary":202,"summary_zh":9,"content":9,"source_name":203,"source_url":9,"published_at":204,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":205,"score_detail":206,"sources":208,"tags":210,"primary_topic":214,"secondary_topics":215,"classification":216,"search_phrases":240,"slug":242,"view_count":139,"doi":243,"paper":244,"created_at":251},1365,"基于数字化水平评估的贵州省农业碳排放影响因素分解","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fenvironmental-science\u002Farticles\u002F10.3389\u002Ffenvs.2026.1901851\u002Fabstract","基于 2013—2022 年贵州省面板数据，使用 LMDI 与 VAR 模型分解数字化对农业碳排放的驱动效应。结果显示：数字化对碳排放由强促增转为弱促增；能源强度与数字化引致的能源效率合计减排 3168 万吨；预测 2023—2030 年可净减排 925 万吨；数字化并非自动等同于减排，需以精准投入管理、畜禽粪污处理和清洁能源为支撑。","Frontiers in Environmental Science · 2026-09","2026-08-31T16:00:00Z",67,{"impact":19,"substance":103,"depth":18,"authority":105,"freshness":139,"relevant":21,"comment":207},"基于省级面板数据的实证研究，揭示数字化对农业碳排放的阶段性影响，为精准减排提供依据。",[209],{"name":203,"url":201},[26,165,211,212,213],"贵州","LMDI","能源效率",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":217},{"candidates":218,"agricultural_evidence":47},[219,224,230,236],{"code":32,"score":220,"supported":47,"title":221,"abstract":222,"tags":223},23,[121,122],[121,122],[165],{"code":225,"score":189,"supported":47,"title":226,"abstract":227,"tags":229},"C",[],[228],"畜禽",[],{"code":231,"score":189,"supported":47,"title":232,"abstract":233,"tags":235},"C6",[],[234],"畜禽粪污",[],{"code":45,"score":21,"supported":14,"title":237,"abstract":238,"tags":239},[],[],[26],[241,187],"农业碳排放 数字农业 能源效率 贵州","农业碳排放数字农业能源效率贵州-1365","10.3389\u002Ffenvs.2026.1901851\u002Fabstract",{"doi":243,"openalex_id":9,"authors":245,"venue":9,"cited_by_count":81,"oa_url":9,"card":246,"direction":87,"ingested_from":147},[],{"tldr":247,"method":248,"finding":249,"direction":87,"opportunity":250},"评估数字化水平对贵州农业碳排放的影响，预测未来减排潜力。","LMDI分解与VAR模型，基于2013-2022年面板数据。","数字化减排效应由强转弱，需精准管理等措施支撑。","可研究数字化与农业减排的耦合机制，探索不同数字化水平下的最优减排路径。","2026-09-02T00:05:06.917066Z",{"id":253,"title":254,"url":255,"summary":256,"summary_zh":9,"content":257,"source_name":258,"source_url":9,"published_at":259,"category":260,"cover_url":9,"hotness":13,"is_selected":14,"score":261,"score_detail":262,"sources":265,"tags":267,"primary_topic":273,"secondary_topics":274,"classification":275,"search_phrases":319,"slug":322,"view_count":81,"doi":9,"paper":9,"created_at":323},5498,"江西省「十五五」农业农村现代化规划印发 部署智慧农业与四季接续","https:\u002F\u002Fjx.ifeng.com\u002Fc\u002F8xPB4YCdgGF","《江西省「十五五」农业农村现代化规划》（赣府发〔2026〕16号）围绕32项重点任务展开，明确推进信息技术与农业深度融合，加快农业人工智能技术创新与场景拓展，建设农业农村大数据平台。规划还部署井冈山国家农业科技园区升建国家农高区，推广「村播」「田播」电商直播带货，并开展农产品碳足迹管理与农业碳交易。","（一）提升粮食等重要农产品供给保障水平。坚持产量产能、生产生态、增产增收一起抓，深入实施藏粮于地、藏粮于技战略，落实新一轮粮食产能提升行动，实施粮油作物大面积单产提升行动，建设吨粮片、高产片，促进良田良种良机良法集成增效。优化农产品种植结构，稳定水稻、油菜生产，大力发展油茶产业，提升大豆产能。优化生猪产能综合调控机制，推进牛羊产业扩量提质，促进畜禽养殖节本提质增效。推动农产品品种培优和品质提升，增加营养、健康、风味农产品供给。完善价格、补贴、保险“三位一体”的种粮农民收益保障机制，建立健全与周边省份粮食产销协作机制，强化粮食产购储加销协同保障。\n\n（二）构建多元化食物供给体系。践行大食物观，坚持农林牧渔并举，全方位、多用途开发食物资源。提升“菜篮子”产业质量和效益，推动蔬菜、水果、茶叶、中药材产业优质发展。丰富畜产品供给，发展蜂、兔、肉鸽、鹌鹑等特色养殖。积极发展大水面生态渔业，科学推进稻渔综合种养。发展林下经济，壮大林药和森林食品。发展富硒食品与功能食品，开发新型食品资源。\n\n（三）加强耕地保护与质量提升。严格落实耕地保护党政同责，严守耕地保护红线。统筹农用地布局优化，严格耕地占补平衡管理，规范补充耕地质量验收。科学开展红壤改良利用，推进酸化耕地治理，分阶段实施耕地有机质提升行动。分区分类高质量推进高标准农田建设，对新建和改造提升实行差异化投入，优先在鄱阳湖平原、赣抚平原、吉泰盆地等区域推进整区域建设，逐步把具备条件的永久基本农田建成高标准农田。完善高标准农田立项、建设、验收和管护机制，编制高标准农田建设全流程工作规范，统一建设标准、施工工艺、管理流程，压实管护责任。开展高标准农田整治提升行动，按照“集中修复一批、改造提升一批、疏通灌溉一批、置换退出一批”分类施策，逐步补齐设施短板、消除存量问题。健全完善问题发现响应处置机制。实施大中型灌区现代化建设与改造，推动井冈灌区、赣抚平原灌区等项目建设，强化高标准农田渠系与灌区骨干工程衔接。\n\n（四）提升农业防灾减灾救灾能力。发展韧性农业，完善农业气象、水文、地质灾害等监测预警机制。推进防洪排涝抗旱设施建设，持续开展农田沟渠修复整治、中小河流和平原涝区治理。完善农机应急救灾设施装备条件,健全和落实救灾机具配置、救灾备荒种子储备和应急调用机制，加快补齐烘干、仓储等设施装备短板。强化病虫疫情和重大动物疫病监测预警、联防联控和应急处置，建强县乡动植物疫病防控专业队伍。","凤凰网江西 2026-10-21","2026-10-21T00:00:00Z","政策",80,{"impact":104,"substance":220,"depth":18,"authority":17,"freshness":263,"relevant":21,"comment":264},9,"省级「十五五」农业农村现代化规划全文，条款与数字细节充实，涉及智慧农业、高标准农田、粮食产能等实质部署，值得进入每日精选。",[266],{"name":258,"url":255},[268,269,270,271,272],"智慧农业","高标准农田","十五五规划","大食物观","粮食产能",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":276},{"candidates":277,"agricultural_evidence":47},[278,285,289,293,299,304,309,314],{"code":279,"score":280,"supported":47,"title":281,"abstract":283,"tags":284},"J2",11,[282],"农业农村现代化",[282],[],{"code":45,"score":263,"supported":47,"title":286,"abstract":287,"tags":288},[268],[],[268],{"code":32,"score":20,"supported":47,"title":290,"abstract":291,"tags":292},[],[122,56],[],{"code":294,"score":189,"supported":47,"title":295,"abstract":296,"tags":298},"I2",[],[297],"农业人工智能",[],{"code":300,"score":21,"supported":14,"title":301,"abstract":302,"tags":303},"A8",[],[],[272],{"code":305,"score":21,"supported":14,"title":306,"abstract":307,"tags":308},"E2",[],[],[271],{"code":310,"score":21,"supported":14,"title":311,"abstract":312,"tags":313},"H1",[],[],[269],{"code":315,"score":21,"supported":14,"title":316,"abstract":317,"tags":318},"J1",[],[],[270],[320,321],"江西 十五五 农业农村现代化规划","江西 高标准农田 鄱阳湖平原","江西十五五农业农村现代化规划-5498","2026-10-22T00:06:58.106992Z",{"id":325,"title":326,"url":327,"summary":328,"summary_zh":329,"content":9,"source_name":154,"source_url":327,"published_at":330,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":331,"score_detail":332,"sources":335,"tags":337,"primary_topic":343,"secondary_topics":348,"classification":355,"search_phrases":413,"slug":416,"view_count":81,"doi":417,"paper":418,"created_at":439},5472,"Turning dirt into gold: the business case of soil carbon for agrarian pathways in arid climate","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1966265","Introduction Agricultural carbon sequestration offers considerable potential for mitigating anthropogenic climate change, particularly in the low-organic-carbon soils of the Middle East. This study presents a foundational scenario-based assessment of soil carbon sequestration and economic feasibility. Materials and methods Crop production and market datasets from Jordanian statistics (1998–2023) were used to estimate potential soil carbon sequestration and economic performance. Using benchmarks from literature in comparable agroecological regions, four soil carbon management practices were evaluated across multiple crops for their carbon storage potential using Monte Carlo uncertainty and sensitivity analysis. Sensitivity of economic performance was assessed under optimistic\u002Fpessimistic scenarios. Results Among the evaluated practices, the highest carbon sequestration rate was achieved through increased Soil Organic Carbon (SOC), reaching a maximum of 27.44 metric tons of CO₂ per hectare per year (tCO₂\u002Fha\u002Fyear) under the high scenario for cucumbers. In contrast, barley showed the highest sequestration rate at 0.45 tCO₂\u002Fha\u002Fyear under increased SOC in the high scenario. Sensitivity analysis showed that ±20% uncertainty in the management coefficients resulted in practice-specific changes of 1.11 tCO₂\u002Fha\u002Fyear for increased SOC and 0.46 tCO₂\u002Fha\u002Fyear for improved cropland management. Horticultural crops, particularly cucumbers and lemons, exhibited the highest sequestration rates and strongest responses to management interventions. Field crops such as wheat, barley, and olives demonstrated lower, more stable sequestration rates. Economic analysis revealed that cucumbers, grapes, potatoes, and Open-field farming (OFF) tomatoes achieved the highest tri-pillar performance (net and adjusted profits and break-even cost) alongside effective carbon sequestration. These findings highlight that crop choice is a primary determinant of both crop profitability and sequestrated carbon potential and feasibility, with significant implications for climate-smart agriculture, carbon credit mechanisms, and intensification strategies in data-scarce regions. Study limitations While empirical field validation remains a future priority, this model-based study provides a screening-level theoretical baseline to guide targeted research and investment in arid agricultural systems.","引言 农业碳封存为缓解人为气候变化提供了可观潜力，尤其是在中东低有机碳土壤中。本研究提出了一项基于情景的土壤碳封存与经济学可行性基础评估。材料与方法 利用约旦统计数据（1998—2023年）中的作物生产和市场数据集，估算潜在的土壤碳封存及经济表现。参考可比农业生态区域文献中的基准值，采用蒙特卡洛不确定性分析和敏感性分析，评估了四种土壤碳管理措施在多种作物上的碳储存潜力。在乐观\u002F悲观情景下评估了经济表现的敏感性。结果 在评估的各项措施中，通过增加土壤有机碳（SOC）实现的碳封存速率最高，在黄瓜的高情景下达到最高27.44公吨二氧化碳\u002F公顷\u002F年（tCO₂\u002Fha\u002Fyear）。相比之下，大麦在增加SOC的高情景下表现出最高的封存速率，为0.45 tCO₂\u002Fha\u002Fyear。敏感性分析表明，管理系数±20%的不确定性导致增加SOC和改善农田管理分别产生1.11 tCO₂\u002Fha\u002Fyear和0.46 tCO₂\u002Fha\u002Fyear的措施特异性变化。园艺作物，尤其是黄瓜和柠檬，表现出最高的封存速率和对管理干预的最强响应。小麦、大麦和橄榄等大田作物表现出较低但更稳定的封存速率。经济分析显示，黄瓜、葡萄、马铃薯和露地栽培（OFF）番茄在有效碳封存的同时实现了最高的三支柱表现（净利润、调整后利润和盈亏平衡成本）。这些发现突出表明，作物选择是作物盈利能力和封存碳潜力及可行性的主要决定因素，对气候智慧型农业、碳信用机制和数据稀缺地区的集约化策略具有重要意义。研究局限性 尽管田间实证验证仍是未来的优先事项，但这项基于模型的研究提供了筛选层面的理论基线，以指导干旱农业系统中的针对性研究和投资。","2026-10-20T00:00:00Z",70,{"impact":105,"substance":103,"depth":333,"authority":158,"freshness":263,"relevant":21,"comment":334},16,"基于约旦1998–2023年统计数据的模型情景研究，量化了不同作物固碳速率与经济收益，对干旱区农业碳汇与碳信用机制有参考价值，但属区域性模拟研究，尚未经田间验证。",[336],{"name":154,"url":327},[338,339,340,341,342],"气候智慧农业","碳交易","干旱农业","土壤碳汇","作物选择",{"code":344,"slug":345,"name":346,"path":347,"parent_code":36},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",[349,354],{"code":350,"slug":351,"name":352,"path":353,"parent_code":36},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":356},{"candidates":357,"agricultural_evidence":47},[358,364,371,376,383,390,396,402,408],{"code":344,"score":359,"supported":47,"title":360,"abstract":361,"tags":363},14,[345],[362,345],"土壤",[],{"code":350,"score":13,"supported":47,"title":365,"abstract":366,"tags":370},[],[367,368,369],"气候变化","干旱","climate change",[338],{"code":32,"score":17,"supported":47,"title":372,"abstract":373,"tags":375},[],[122,374],"carbon sequestration",[341,339],{"code":377,"score":20,"supported":47,"title":378,"abstract":379,"tags":382},"A2",[],[380,381],"小麦","wheat",[],{"code":384,"score":20,"supported":47,"title":385,"abstract":386,"tags":389},"B1",[],[387,388],"番茄","黄瓜",[],{"code":391,"score":189,"supported":47,"title":392,"abstract":393,"tags":395},"A5",[],[394],"马铃薯",[],{"code":397,"score":189,"supported":47,"title":398,"abstract":399,"tags":401},"B2",[],[400],"葡萄",[],{"code":403,"score":189,"supported":47,"title":404,"abstract":405,"tags":407},"H6",[],[406],"农业生态",[],{"code":45,"score":189,"supported":47,"title":409,"abstract":410,"tags":412},[],[411],"smart agriculture",[],[414,415],"约旦 土壤碳汇 黄瓜","土壤固碳 经济可行性","约旦土壤碳汇黄瓜-5472","10.3389\u002Ffsufs.2026.1966265",{"doi":417,"openalex_id":419,"authors":420,"venue":154,"cited_by_count":81,"oa_url":327,"card":433,"direction":438,"ingested_from":90},"W7223064172",[421,423,425,428,430],{"name":422,"orcid":9},"Tasnim Althalaj",{"name":424,"orcid":9},"Ross. M. Thompson",{"name":426,"orcid":427},"Tala H. Qtaishat","https:\u002F\u002Forcid.org\u002F0000-0002-2700-6686",{"name":429,"orcid":9},"Dan Bumblauskas",{"name":431,"orcid":432},"Fayha Al-Shibli","https:\u002F\u002Forcid.org\u002F0000-0002-5628-0975",{"tldr":434,"method":435,"finding":436,"direction":87,"opportunity":437},"基于约旦1998–2023年数据，评估干旱区土壤固碳潜力与经济可行性。","用蒙特卡洛不确定性与敏感性分析，评估四种土壤碳管理措施在多作物下的表现。","黄瓜等园艺作物固碳率最高，作物选择是固碳与盈利的关键决定因素。","数据稀缺干旱区可结合田间验证与碳信用机制，研究作物级固碳经济激励设计。","智慧农业 \u002F 农业物联网","2026-10-21T23:30:07.383006Z",{"id":441,"title":442,"url":443,"summary":444,"summary_zh":445,"content":9,"source_name":154,"source_url":443,"published_at":330,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":446,"score_detail":447,"sources":449,"tags":451,"primary_topic":457,"secondary_topics":458,"classification":460,"search_phrases":477,"slug":480,"view_count":81,"doi":481,"paper":482,"created_at":525},5467,"Temperature-directed valorisation of coconut frond through pyrolysis for carbon sequestration and sustainable agricultural residue management","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1966170","Coconut frond, an abundantly generated yet underutilized agricultural residue, was pyrolysed at 350 °C, 450 °C, and 550 °C alongside traditionally produced biochar (BC Trad) to evaluate the effects of pyrolysis temperature on physicochemical characteristics, fuel properties, carbon stability, and residue-valorisation potential. Comprehensive characterization using proximate and ultimate analyses, BET\u002FBJH, FTIR, XRD, Raman spectroscopy, SEM-EDS, total pyrogenic carbon (TPC), estimated CO 2 -equivalent stable-carbon retention potential, fuel and energy indices, and 90-day carbon mineralization incubation revealed pronounced temperature-dependent transformations. Biochar carbon recovery was highest at 350 °C (78.91%), demonstrating greater retention of feedstock carbon at lower pyrolysis temperature, whereas increasing temperature progressively enhanced carbon concentration, fixed carbon, thermostable carbon fraction, porosity, and surface area. BET surface area increased from 4.45 m 2 g −1 in BC Trad to 16.67 m 2 g −1 in BC550, accompanied by progressive pore refinement and depletion of oxygen-containing functional groups. Raman spectroscopy showed a decline in ID\u002FIG from 1.28 to 0.80, consistent with increasing structural ordering and aromatic condensation at higher temperatures. Fuel quality also improved with increasing temperature, with higher heating value increasing from 20.72 to 25.56 MJ kg −1 and fuel ratio from 1.49 to 4.26. TPC and estimated CO 2 -equivalent stable-carbon retention potential were highest at BC350 (0.29 kg C kg −1 feedstock and 0.86 kg CO 2 kg −1 feedstock, respectively), whereas BC550 exhibited the lowest apparent net soil-system carbon mineralization among the biochars during the 90-day incubation. Separately, its lower H\u002FC and O\u002FC ratios corresponded to a modelled BC +100 stability index of 0.720 and an estimated mean residence time of 347.61 years, providing model-derived indications of longer-term carbon persistence. Overall, the findings demonstrate a clear temperature-dependent trade-off between carbon recovery and carbon stability, with BC350 providing greater structural recalcitrance and apparent short-term mineralization resistance; the optimal pyrolysis temperature therefore depends on the intended end use rather than a single treatment being universally superior. Together, the short-term mineralization results and model-derived stability estimates indicate distinct dimensions of carbon persistence that should be considered when optimizing coconut-frond pyrolysis for sustainable agricultural-residue management, durable soil-carbon sequestration, and circular bioeconomy applications.","椰子叶是一种大量产生但未充分利用的农业废弃物，其在350 °C、450 °C和550 °C下热解，并与传统生产的生物炭（BC Trad）进行对比，以评估热解温度对理化特性、燃料性质、碳稳定性及残余物增值潜力的影响。采用工业分析和元素分析、BET\u002FBJH、FTIR、XRD、拉曼光谱、SEM-EDS、总热解碳（TPC）、估算CO₂当量稳定碳保留潜力、燃料与能量指标以及90天碳矿化培养进行综合表征，揭示了显著的温度依赖性变化。生物炭碳回收率在350 °C时最高（78.91%），表明较低热解温度下原料碳的保留更多，而升高温度则逐步提高碳浓度、固定碳、热稳定碳组分、孔隙率和比表面积。BET比表面积从BC Trad的4.45 m² g⁻¹增至BC550的16.67 m² g⁻¹，同时伴随孔隙细化和含氧官能团的消耗。拉曼光谱显示ID\u002FIG从1.28降至0.80，与较高温度下结构有序性和芳香缩合度增加相一致。燃料品质亦随温度升高而改善，高位热值从20.72增至25.56 MJ kg⁻¹，燃料比从1.49增至4.26。TPC和估算CO₂当量稳定碳保留潜力在BC350时最高（分别为0.29 kg C kg⁻¹原料和0.86 kg CO₂ kg⁻¹原料），而BC550在90天培养期间表现出生物炭中最低的表观净土壤系统碳矿化。此外，其较低的H\u002FC和O\u002FC比对应模拟BC +100稳定性指数为0.720，估算平均停留时间为347.61年，提供了模型推导的较长时期碳持久性指示。总体而言，研究结果揭示了碳回收与碳稳定性之间明显的温度依赖性权衡，BC350表现出更高的结构惰性和表观短期抗矿化性；因此，最佳热解温度取决于预期最终用途，而非单一处理普遍优越。短期矿化结果与模型推导的稳定性估算共同表明，碳持久性具有不同维度，应予考虑。",74,{"impact":105,"substance":104,"depth":18,"authority":158,"freshness":263,"relevant":21,"comment":448},"该研究系统考察了热解温度对椰叶生物炭碳回收与碳稳定性的权衡关系，数据翔实、结论可靠，对农业废弃物资源化与土壤固碳具有参考价值，但属于细分领域基础研究，公共影响有限。",[450],{"name":154,"url":443},[452,453,454,455,456],"生物炭","农业废弃物","秸秆利用","土壤固碳","热解技术",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[459],{"code":344,"slug":345,"name":346,"path":347,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":461},{"candidates":462,"agricultural_evidence":47},[463,467,471],{"code":32,"score":280,"supported":47,"title":464,"abstract":465,"tags":466},[374],[374],[],{"code":344,"score":17,"supported":47,"title":468,"abstract":469,"tags":470},[],[362,345],[455,452],{"code":472,"score":473,"supported":47,"title":474,"abstract":475,"tags":476},"H5",5,[],[453],[453,454],[478,479],"椰叶 热解 生物炭","生物炭 碳封存 农业废弃物","椰叶热解生物炭-5467","10.3389\u002Ffsufs.2026.1966170",{"doi":481,"openalex_id":483,"authors":484,"venue":154,"cited_by_count":81,"oa_url":519,"card":520,"direction":87,"ingested_from":90},"W7223016223",[485,488,490,493,496,498,501,504,506,509,511,514,517],{"name":486,"orcid":487},"Morasanahalli Lokesha Sachin","https:\u002F\u002Forcid.org\u002F0009-0008-8615-0822",{"name":489,"orcid":9},"Yogananda Shivalli Boregowda",{"name":491,"orcid":492},"Matadadoddi Nanjundegowda Thimmegowda","https:\u002F\u002Forcid.org\u002F0000-0001-7632-1036",{"name":494,"orcid":495},"M. A. Ananthakumar","https:\u002F\u002Forcid.org\u002F0000-0003-4376-9541",{"name":497,"orcid":9},"Pudiyanda Shadali Fathima",{"name":499,"orcid":500},"C. M. Sunil","https:\u002F\u002Forcid.org\u002F0000-0002-9625-5686",{"name":502,"orcid":503},"K. M. Pravalika","https:\u002F\u002Forcid.org\u002F0009-0004-5258-392X",{"name":505,"orcid":9},"M.C. Anjali",{"name":507,"orcid":508},"Sathisha Govinakere Sambegowda","https:\u002F\u002Forcid.org\u002F0009-0000-8620-6461",{"name":510,"orcid":9},"B. Divya",{"name":512,"orcid":513},"M. K. Shruthi","https:\u002F\u002Forcid.org\u002F0000-0003-3703-9817",{"name":515,"orcid":516},"Santosh Onte","https:\u002F\u002Forcid.org\u002F0009-0002-8367-1303",{"name":518,"orcid":9},"Sanjeev Kumar","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1966170\u002Fpdf",{"tldr":521,"method":522,"finding":523,"direction":87,"opportunity":524},"研究不同热解温度对椰叶生物炭碳固存与稳定性的影响。","350\u002F450\u002F550°C热解椰叶，多手段表征并做90天矿化培养。","碳回收率与碳稳定性随温度呈权衡，最佳温度取决于用途。","可探索热解温度与还田情景耦合的长期碳固存模型及经济性评估。","2026-10-21T23:30:07.083785Z"]